Proximal linearized alternating direction method of multipliers algorithm for nonconvex image restoration with impulse noise

Image restoration with impulse noise is an important task in image processing. Taking into account the statistical distribution of impulse noise, the l(1)-norm data fidelity and total variation (l(1)TV) model has been widely used in this area. However, the l(1)TV model usually performs worse when the noise level is high. To overcome this drawback, several nonconvex models have been proposed. In this paper, an efficient iterative algorithm is proposed to solve nonconvex models arising in impulse noise. Compared to existing algorithms, the proposed algorithm is a completely explicit algorithm in which every subproblem has a closed-form solution. The key idea is to transform the original nonconvex models into an equivalent constrained minimization problem with two separable objective functions, where one is differentiable but nonconvex. As a consequence, the proximal linearized alternating direction method of multipliers is employed to solve it. Extensive numerical experiments are presented to demonstrate the efficiency and effectiveness of the proposed algorithm.